def each_subj_seperate(main_subj_folder, mni_atlas_file_name, idx, atlas_type):
for sub in glob.glob(f'{main_subj_folder}{os.sep}*{os.sep}'):
sn = sub.split(os.sep)[-2]
num_mat_name = sub + 'non-weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
if os.path.exists(num_mat_name):
num_mat = np.load(num_mat_name)
ncb_num = get_node_betweenness_centrality(num_mat)
add_mat_name = sub + 'weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
add_mat = np.load(add_mat_name)
ncb_add = get_node_betweenness_centrality(add_mat)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_num, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'Num_node-centrality-betweenness_' + atlas_type,
sub[:-1])
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_add, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'ADD_node-centrality-betweenness_' + atlas_type,
sub[:-1])
def grouped_together(main_subj_folder, mni_atlas_file_name, idx, atlas_type):
ncb_num = []
ncb_add = []
for sub in glob.glob(f'{main_subj_folder}{os.sep}*{os.sep}'):
sn = sub.split(os.sep)[-2]
num_mat_name = sub + 'non-weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
if os.path.exists(num_mat_name):
num_mat = np.load(num_mat_name)
ncb_num.append(get_node_betweenness_centrality(num_mat))
add_mat_name = sub + 'weighted_wholebrain_5d_labmask_yeo7_200_nonnorm.npy'
add_mat = np.load(add_mat_name)
ncb_add.append(get_node_betweenness_centrality(add_mat))
ncb_num = np.asarray(ncb_num)
ncb_add = np.asarray(ncb_add)
ncb_num[ncb_num == 0] = np.nan
ncb_add[ncb_add == 0] = np.nan
ncb_num_mean = np.nanmean(ncb_num, axis=0)
ncb_add_mean = np.nanmean(ncb_add, axis=0)
ncb_num_mean[np.isnan(ncb_num_mean)] = 0
ncb_add_mean[np.isnan(ncb_add_mean)] = 0
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_num_mean, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'Num_node-centrality-betweenness_' + atlas_type,
main_subj_folder)
weighted_by_atlas, weights_dict = weight_atlas_by_add(
mni_atlas_file_name, ncb_add_mean, idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name,
f'ADD_node-centrality-betweenness_' + atlas_type,
main_subj_folder)
eff_add_dict = merge_dict(eff_add_dict, eff_add)
wos1.append(table1.find_value_by_scan_Language('Word Order Score 1', sn))
lws.append(table1.find_value_by_scan_Language('Learning words slope', sn))
eff_num_mat =np.zeros((n_subj,len(eff_num_dict)))
eff_add_mat = np.zeros((n_subj,len(eff_add_dict)))
for k in eff_num_dict.keys():
eff_num_mat[:, k] = eff_num_dict[k]
for k in eff_add_dict.keys():
eff_add_mat[:, k] = eff_add_dict[k]
volume_type = 'Num'
r, p = calc_corr(wos1,eff_num_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, f'{volume_type}_LocEff-WOS_th_r_'+atlas_type, main_subj_folders)
r, p = calc_corr(lws,eff_num_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, f'{volume_type}_LocEff-LWS_th_r_'+atlas_type, main_subj_folders)
volume_type = 'ADD'
r, p = calc_corr(wos1,eff_add_mat, fdr_correct=False, remove_outliers=True)
weighted_by_atlas,weights_dict = weight_atlas_by_add(mni_atlas_file_name,r,idx)
save_as_nii(weighted_by_atlas, mni_atlas_file_name, f'{volume_type}_LocEff-WOS_th_r_'+atlas_type, main_subj_folders)
r, p = calc_corr(lws,eff_add_mat, fdr_correct=False, remove_outliers=True)
eff_num_mat = np.zeros((len(num_ne_dict[0]), len(num_ne_dict)))
eff_add_mat = np.zeros((len(add_ne_dict[0]), len(add_ne_dict)))
for k in num_ne_dict.keys():
eff_num_mat[:, k] = num_ne_dict[k]
for k in add_ne_dict.keys():
eff_add_mat[:, k] = add_ne_dict[k]
idx = np.load(r'F:\data\V7\HCP\cm_num_lookup.npy')
mni_atlas_file_name = r'F:\data\atlases\aal300\AAL150_fixed.nii'
nii_base = r'F:\data\atlases\yeo\yeo7_1000\Schaefer2018_1000Parcels_17Networks_order_FSLMNI152_2mm.nii'
main_subj_folders = r'F:\data\V7\HCP'
r, p = calc_corr_mat(eff_num_mat, eff_add_mat, fdr_correct=True)
weighted_by_atlas, weights_dict = weight_atlas_by_add(mni_atlas_file_name, r,
idx)
save_as_nii_aal(weighted_by_atlas, mni_atlas_file_name, nii_base,
f'aal300_LocEff_correlation_ADD_Num_fdr', main_subj_folders)
r, p = calc_corr_mat(eff_num_mat, eff_add_mat, fdr_correct=False)
weighted_by_atlas, weights_dict = weight_atlas_by_add(mni_atlas_file_name, r,
idx)
save_as_nii_aal(weighted_by_atlas, mni_atlas_file_name, nii_base,
f'aal300_LocEff_correlation_ADD_Num', main_subj_folders)
eff_add_mean = np.nanmean(eff_add_mat, axis=0)
weighted_by_atlas, weights_dict = weight_atlas_by_add(mni_atlas_file_name,
eff_add_mean, idx)
save_as_nii_aal(weighted_by_atlas, mni_atlas_file_name, nii_base,
f'aal300_LocEff_ADD', main_subj_folders)
